Anna Jonnson is a Senior research engineer at Department of
Computing Science at Umeå University.
Abstract: When modelling a natural language, there is already a
number of predefined (but not necessarily static) rules for how
to use that language, such as its grammar. Deep learning methods
have been shown successful when applied to various language
modelling tasks. At the same time, applying deep learning gives
us result-producing black boxes with no possibility of providing
a reasoning for each of the results. Rule-based language models
are transparent in that we can either output the rule sequence
used as part of the result, or apply the rules in a backwards
fashion to explain the result. They also have the advantage that
they can make use of information that is already available to
us. In this talk, we look at semantic modelling in particular,
and explore some of the challenges in rule-based semantic
modelling.